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At least 289 records · Page 16Linked to original sources
PubMed2006

Risk- and robustness-based solutions to a multi-objective water distribution system rehabilitation problem under uncertainty .

The water distribution system (WDS) rehabilitation problem is defined here as a multi-objective optimisation problem under uncertainty. Two alternative problem formulations are considered. The first objective in both approaches is to minimise the total rehabilitation cost. The second objective is to either maximise the overall WDS robustness or to minimise the total WDS risk. The WDS robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. Total risk is defined as the sum of nodal risks, where nodal risk is defined as the product of the probability of pressure failure at that node and consequence of such failure. Decision variables are the alternative rehabilitation options for each pipe in the network. The only source of uncertainty is the future water consumption. Uncertain demands are modelled using any probability density functions (PDFs) assigned in the problem formulation phase. The corresponding PDFs of the analysed nodal heads are calculated using the Latin Hypercube sampling technique. The optimal rehabilitation problem is solved using the newly developed rNSGAII method which is a modification of the well-known NSGAII optimisation algorithm. In rNSGAII a small number of demand samples are used for each fitness evaluation leading to significant computational savings when compared to the full sampling approach. The two alternative approaches are tested, verified and their performance compared on the New York tunnels case study. The results obtained demonstrate that both new methodologies are capable of identifying the robust (near) Pareto optimal fronts while making significant computational savings.

Facility Design and Construction↗
PubMed2004

Metabolic syndrome and robustness tradeoffs.

The metabolic syndrome is a highly complex breakdown of normal physiology characterized by obesity, insulin resistance, hyperlipidemia, and hypertension. Type 2 diabetes is a major manifestation of this syndrome, although increased risk for cardiovascular disease (CVD) often precedes the onset of frank clinical diabetes. Prevention and cure for this disease constellation is of major importance to world health. Because the metabolic syndrome affects multiple interacting organ systems (i.e., it is a systemic disease), a systems-level analysis of disease evolution is essential for both complete elucidation of its pathophysiology and improved approaches to therapy. The goal of this review is to provide a perspective on systems-level approaches to metabolic syndrome, with particular emphasis on type 2 diabetes. We consider that metabolic syndromes take over inherent dynamics of our body that ensure robustness against unstable food supply and pathogenic infections, and lead to chronic inflammation that ultimately results in CVD. This exemplifies how trade-offs between robustness against common perturbations (unstable food and infections) and fragility against unusual perturbations (high-energy content foods and low-energy utilization lifestyle) is exploited to form chronic diseases. Possible therapeutic approaches that target fragility of emergent robustness of the disease state have been discussed. A detailed molecular interaction map for adipocyte, hepatocyte, skeletal muscle cell, and pancreatic beta-cell cross-talk in the metabolic syndrome can be viewed at http://www.systems-biology.org/001/003.html.

Diabetes Mellitus, Type 2↗
PubMed1992

Robust design: a new tool for health care quality?

Robust design is a powerful technique for developing processes that produce desirable outcomes, even in the presence of factors that cannot be controlled or cannot be controlled economically. In the past 12 years several leading high-technology manufacturing companies in the United States have applied robust design methods with considerable success. This article discusses the basic concepts of robust design and speculates on how these ideas might be applied to health care quality management.

Health Services Research↗
PubMed2002

Non-linear transform-based robust adaptive latency change estimation of evoked potentials.

OBJECTIVES: To improve the latency change estimation of evoked potentials (EP) under the lower order alpha-stable noise conditions by proposing and analyzing a new adaptive EP latency change detection algorithm (referred to as the NLST). METHODS: The NLST algorithm is based on the fractional lower order moment and the nonlinear transform for the error function. The computer simulation and data analysis verify the robustness of the new algorithm. RESULTS: The theoretical analysis shows that the iteration equation of the NLST transforms the lower order alpha-stable process en (k) into a second order moment process by a nonlinear transform. The simulations and the data analysis showed the robustness of the NLST under the lower order alpha-stable noise conditions. CONCLUSIONS: The new algorithm is robust under the lower order alpha-stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate the alpha value of the EP signals and noises.

Algorithms↗
PubMed2003

[Tests for robustness of biomedical and pharmaceutical analytic methods].

In biomedical and pharmaceutical analysis, especially in the analyses executed in the pharmaceutical industry, the quality of the results obtained is strictly evaluated and controlled. Therefore, newly developed methods of analysis are subjected to a strict and vast method validation. One of the validation criteria studied is the determination of the robustness of the method. This is done by means of a robustness test. In this manuscript the different steps required to set up and interpret a robustness test are discussed and illustrated with a number of examples.

Chemistry Techniques, Analytical↗
PubMed2004

[Developing robust near infrared calibration models].

There are three approaches to developing robust near infrared calibration models, including spectral pretreatment such as differentiation, Piecewise Multiplicative Scatter Correction (PMSC), Finite Impulse Response (FIR), and Orthogonal Signal Correction (OSC), to remove external variations, selecting wavelengths which are insensitive to external variations, and constructing temperature-hybrid calibration models. In this paper, these three strategies were investigated based on reforming gasoline NIR spectra collected at different temperatures in order to develop robust RON and benzene calibration models against temperature. It has been found that with only spectral pretreatment even OSC method fails to obtain satisfactory results, which could not remove the effects caused by temperature fluctuation. Selecting wavelengths by genetic algorithms and constructing temperature-hybrid calibration models, in which spectra measured at different temperature are combined into one calibration set, are both good approaches to developing robust NIR calibration models against temperature. The latter seems better because it needs no special knowledge and extra software, but thenon-linear effects should be considered in practical applications.

Calibration↗
PubMedDate not supplied

[Estimation of reproducibility and repeatability in microbiological ring trials--robust versus conservative methods].

During the last years there was a lot of progress to be seen in the development of standardized methods for microbiological ring trials. The statistical analyzing strategies, in particular the calculation of estimations for the parameters repeatability and reproducibility, will be considered in this paper. Apart from the conservative method of the variance analysis robust methods are increasingly discussed. We will compare and discuss these methods using data of recently realized ring trials. If we can assume a normal distribution of our data, then all estimations are theoretically precise and efficient. But up to now, we know very little about the character of the robust estimations, if the normal distribution cannot be assumed. In addition to this, we have to mention once more, that the use of robust estimators is unreasonable without taking a critical look on the data themselves. Thus, we will show the possibilities of graphical presentation of all data to identify laboratories with critical results.

Animals↗
PubMed2005

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

Biomarkers, Tumor↗
PubMed2005

[Study on the robust NIR calibration models for moisture].

The differences in sample moisture affect the robustness of NIR model obviously. In the present paper, three approaches, including preprocessing spectra, selecting wavelength, and setting up global calibration, were investigated to analyze the feasibility of setting up robust calibrations based on the protein content of wheat with different moistures. It has been found that with only spectral pretreatment method it fails to obtain satisfactory results, which can not remove the effects caused by moisture difference. Both selecting wavelengths and developing global calibration model proved to be good approaches for developing robust NIR calibration, yet developing global calibration is better. The mechanisms of the three different methods were also analyzed theoretically.

Calibration↗
PubMed1994

Robust estimates of wildlife location using telemetry data.

The location of wildlife is frequently determined using telemetry data gathered at short intervals. If radio transmissions are reflected, as often occurs in mountainous regions, then existing location estimation techniques re unreliable. We explore the effects of gross observation errors upon current analyses and suggest an alternative analysis based on robust state-space time-series modeling. We determine location estimates and their precisions, both for simulated and real mule-deer data, using current and robust procedures. Implementation and specification of filter parameters are also discussed. We conclude that the proposed filter-smoother is similar to the Gaussian filter-smoother when data are not greatly contaminated and that the robust version improves upon location estimates when contamination is large.

Animal Identification Systems↗
PubMed1993

A manufacturer's approach to development of matrix robust methods.

Use of matrix robust methods (MRM) to solve the problem of matrix-induced analytic errors appears to be a sound strategy. The development of MRM, however, is in an infancy stage. The principal barrier is the complexity of the matrix effect that involves interactions of the matrix, the analyte, and the technology base of the test method. Each of these three components has its own set of variables. The present article focuses on concepts and tactics to develop the MRM that appear promising on a path-forward basis. The author believes that the current environment favors probability of successful development of MRM. The quality awareness at all functional levels is high, technically feasible models for the design and development of MRM exist, and commercialization of such a method promises the developer a competitive advantage in the marketplace. The optimum strategy for MRM development appears to be evolutionary, ie, starting with a few critical methods and the samples representing the prevalent matrix types. Success in developing MRM also depends on close cooperation between the developers of the MRM, proficiency testing material, the proficiency testing providers, and the regulatory bodies. The research and development program may also include approaches that detect and/or correct the matrix-caused error(s) both in place of, or as an adjunct to, MRM. With respect to the development of genuine MRM, the author has given a typical development scenario comprising the design specifications, specific experimental approaches, evaluation, market introduction, and postintroduction monitoring of its robustness. The crux of the experimental approach is the response surface co-optimization of reaction conditions for the samples of prevalent matrix types such as the proficiency testing materials. The recommended approach is supported by examples of existing methods that exhibit robustness against certain types of matrices. The author believes that addition of MRM to the clinical chemistry methods repertoire is likely to improve the test result quality. It will also improve proficiency testing performance and patient care while boosting the morale of laboratory personnel.

Bias↗
PubMed1998

An efficient, robust, and unified method for mapping complex traits (II): multipoint linkage analysis.

Extending the method for two-point linkage analysis [Zhao et al., 1998: Am J Med Genet 77:366-383], this paper introduces a semiparametric method for multipoint linkage analysis, expected to gain efficiency by using multiple markers simultaneously. Overcoming the longstanding statistical and computational challenge to the parametric approaches (or lod score methods) for multipoint linkage analysis, this semiparametric approach, based on the estimating equation technique, yields statistically efficient and yet robust estimates and enjoys the computational efficiency in processing multiple markers from large pedigrees. Its computational burden increases linearly with the sizes of pedigrees and with the number of marker loci. To illustrate this semiparametric method, we apply it to marker data gathered for the Breast Cancer Consortium. The result supports the earlier finding of the positive linkage with BRCA1 and has also shown that the multipoint linkage analysis has an improved power. In addition, we have applied this method to analyze genome scanning data that have been used to localize genes responsible for type 1 diabetes. In support of the earlier findings, the genome scanning detects the linkage signals on chromosome 6 but does not support the earlier suggestions of two major genes in that genome segment. Through sensitivity analysis, it appears that the results are robust to misspecification of penetrance and allele frequency.

Automation↗
PubMed1996

Robust procedures for analysing a two-period cross-over design with baseline measurements.

Patel analysed a two-period cross-over design with baseline measurements assuming bivariate normality for the joint distribution of the period responses. In this paper, we propose non-parametric methods for analysing this design, including the use of the Wilcoxon rank sum test to derive the preliminary tests from the baseline measurements. We fit a robust regression line of the treatment response on baseline for each period and compute residuals. We also fit a robust locally weighted regression as an alternative method for computing residuals. Then, following Koch's procedure, we analyse the residuals for testing the significance of the treatment x period interaction and the treatment difference. We provide a numerical example to illustrate the methods.

Clinical Trials, Phase I as Topic↗
PubMed1996

Efficiency robust tests of independence in contingency tables with ordered classifications.

Ordered categorical data occur frequently in biomedical research. The linear by linear association test for ordered R x C tables permits the investigator to specify row and column scores for analysis. When an investigator believes that there may be more than one set of reasonable scores or when more than one investigator proposes scores, we need a method to decide upon a single procedure to use. We show how to use efficiency robustness principles to combine tests from two or more sets of scores into one robust test for analysis. This test minimizes the worst possible efficiency loss over all the sets of scores. We illustrate the methodology for the R x C case and, in detail, for the important special 2 x C case.

Alcohol Drinking↗
PubMed1996

Evaluation of long-term survival: use of diagnostics and robust estimators with Cox's proportional hazards model.

We consider methodological problems in evaluating long-term survival in clinical trials. In particular we examine the use of several methods that extend the basic Cox regression analysis. In the presence of a long term observation, the proportional hazard (PH) assumption may easily be violated and a few long term survivors may have a large effect on parameter estimates. We consider both model selection and robust estimation in a data set of 474 ovarian cancer patients enrolled in a clinical trial and followed for between 7 and 12 years after randomization. Two diagnostic plots for assessing goodness-of-fit are introduced. One shows the variation in time of parameter estimates and is an alternative to PH checking based on time-dependent covariates. The other takes advantage of the martingale residual process in time to represent the lack of fit with a metric of the type 'observed minus expected' number of events. Robust estimation is carried out by maximizing a weighted partial likelihood which downweights the contribution to estimation of influential observations. This type of complementary analysis of long-term results of clinical studies is useful in assessing the soundness of the conclusions on treatment effect. In the example analysed here, the difference in survival between treatments was mostly confined to those individuals who survived at least two years beyond randomization.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1997

Non-iterative robust estimators of variance components in within-subject designs.

Classic estimators of variance components break down in the presence of outliers and perform less efficiently under non-normality. In this article I present simple non-iterative estimators of variance components that are resistant to outliers and robust to systematic departures from normality, such as heavy tailedness of the distribution of responses. The proposed estimators are based on a robust extension of Hocking's AVE approach and are thus called RAVE estimators. I present results from a Monte Carlo comparison of RAVE versus classic estimation methods including maximum likelihood (ML), restricted maximum likelihood (REML) and minimum variance quadratic unbiased estimation (MIVQUE). Under simulated deviations from normality, RAVE estimators are associated with smaller mean squared errors than all the comparators, and in the normal case they exhibit a minimal loss in relative efficiency. A numerical example illustrates the proposed methodology.

Analysis of Variance↗
PubMed2000

A robust mixed linear model analysis for longitudinal data.

This paper describes robust procedures for estimating parameters of a mixed effects linear model as applied to longitudinal data. In addition to fixed regression parameters, the model incorporates random subject effects to accommodate between-subjects variability and autocorrelation for within-subject variability. Robust empirical Bayesian estimation of subject effects is briefly discussed. As an illustration, the procedures are applied to data from a multiple sclerosis clinical trial.

Adjuvants, Immunologic↗
PubMed1984

Variations of rat skull bone robusticity evoked by malnutrition.

Weanling Holtzman rats of both sexes were fed a control (25% protein), a 10% protein, and a 2% protein semisynthetic diet. Protein deficit (PD) and protein calorie malnutrition (PCM) were estimated from comparisons between control and 10% protein, and control and 2% protein-fed animals, respectively. Animals were killed when they were 56 days old and their skulls cleaned and disarticulated. Individual bones and incisors were ovendried to constant weight. Total weight (TW), maximal projected length (MPL), and robusticity index (RI) were determined on each bone and incisor. It was found that all the bones and incisors did not behave uniformly. They followed two main patterns: (1) Proportional variation. RI values were not affected by nutritional deficiencies. All basicranial bones and 4 of 10 facial bones followed this pattern. (2) Non-proportional variation. RI values were affected by nutritional deficiencies. This pattern was subdivided into two trends: (2a) PD-diminished RI values. Both upper and lower incisors and 1 of 10 facial bones followed this trend. (2b) PCM, but not PD, decreased RI values. All vault bones and the remaining five facial bones followed this trend. It was concluded that there was a differential robusticity response among cranial base, calvaria, and incisors. This response may be connected with the differences in both histogenetic characteristics of those components and the functional roles they have to perform. The nonvault intramembranous bones showed a nonspecific behavior. This fact precluded the classification of the facial region in some of the previously defined patterns.

Analysis of Variance↗